The present invention relates to micro sensors and, more particularly, to biomolecular sensor array design.
Micro sensors and, more particularly, biosensors have attracted much attention lately due to their increasing utility in the pharmaceutical, chemical and biological arenas. Biosensors have been developed to detect a variety of biomolecular complexes including oligonucleotide pairs, antibody-antigen, hormone-receptor, enzyme-substrate and lectin-glycoprotein interactions, for example. In general, biosensors are comprised of two components: a molecular recognition element and a transducing structure that converts the molecular recognition event into a quantifiable signal. Signal transductions are generally accomplished with electrochemical, field-effect transistor, optical absorption, fluorescence or interferometric devices.
Generally, an array of biosensors are used for the execution of biomedical and biomolecular measurements in which the state of the biological system is translated into a response at a specific sensor location. Protein-microarrays or DNA-microarrays are examples of biomolecular sensor arrays. Biomolecular sensor arrays are comprised of individual sensors cells organized in some fashion, such as on a rectangular grid.
The output of the biomolecular sensor array is multidimensional data in which each sensor cell (i.e. each data point in the array) codes the response of a specific experiment. The number of sensor cells, i.e. of events to be measured, can be large, e.g. 10,000. Though there have been many advancements in the area of biosensors, in practice, biomolecular sensor arrays tend to provide noisy data. The noise in the data limits the reliability of conclusions that can be drawn from the measurements. Widespread application of current biomolecular sensor arrays is hampered by the unreliability of the obtained data and the poor reproducibility of results.
Two techniques commonly used to improve on the reliability of the obtained data of the biomolecular sensor arrays include signal processing methods and data averaging methods. Statistical signal processing methods, such as principal component analysis, are applied after recording of the noisy measurements. These methods are intended to detect improbable values at individual sensor cells, such as outliers. Although theses methods may be able to detect if a value at an individual sensor cell is unreliable, they cannot recover the value itself.
Data averaging methods average data over repeated instances of the same measurement at several sensor cells and applies the average to reduce noise in the individual measurements. Averaging over a number of repeated instances of the same measurement at a number of sensor cells can reduce certain types of noise in the individual measurements. For example, random additive Gaussian noise can be reduced by a factor of approximately 1/n. However, a substantial noise reduction would require a high redundancy in the measurements which would be prohibitively expensive. Furthermore, averaging does not improve on certain types of noise, such as multiplicative noise or systematic errors.
The present invention achieves technical advantages as an apparatus and method of reducing noise associated with biomolecular measurement systems. Sensor detection system noise characteristics in the presence of other sensor detection systems are determined and advantageously used to determine an arrangement of the individual sensor cells. The sensor cells are arranged on a substrate such that the system noise is determinable and can thus be filtered from the measurement signal.